Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Applied Time Series and Forecasting ECON5119

  • Academic Session: 2021-22
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

Applied Time Series and Forecasting provides students with a robust statistical framework for assessing the behaviour of time series, such as asset prices. The course provides a balance of theoretical, computational and empirical concepts. There is equal number of lectures and computer labs, in order to ensure the students get hands-on experience with the stylized facts of various financial datasets. 

Timetable

One two-hour lecture per week for 10 weeks.

One two-hour computer lab per week for 10 weeks.

Requirements of Entry

Please refer to the current postgraduate prospectus at: http://www.gla.ac.uk/postgraduate/

Excluded Courses

None

Co-requisites

None

Assessment

Assessment

Course Aims

The main aim of this course is to provide students with all the tools necessary for the analysis of past time series data, in order to be able to forecast the future and help make financial decisions under uncertainty. The lectures teach how to define appropriate advanced statistical models for temporal dependence in financial data, as well as examining the relevant theory. The labs focus on advanced estimation and simulation techniques, and at the same time they allow students to gain hands-on experience with the stylized facts and time properties of different financial datasets (exchange rates, yield curve modelling, inflation, stock prices, oil prices etc).

Intended Learning Outcomes of Course

By the end of this course students will be able to:

1. Devise the theoretical setting as well as the empirical stylized facts that pertain to different financial datasets.

2. Build appropriate statistical models and techniques for modelling such datasets.

3. Programme advanced algorithms in MATLAB that allow estimation and statistical inference.

4. Evaluate statistical estimates, forecasts and other time-series projections, in order to make financial decisions under uncertainty.

5. Collaborate effectively within a group work environment.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.